Functional Regularized Least Squares Classi cation with Operator-valued Kernels

Hachem Kadri (1) , Asma Rabaoui (2) , Philippe Preux (1, 3) , Emmanuel Duflos (1, 4) , Alain Rakotomamonjy (5)

1 SEQUEL - Sequential Learning
2 IMS - Laboratoire de l'intégration, du matériau au système
3 LIFL - Laboratoire d'Informatique Fondamentale de Lille
4 LAGIS-SI
5 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functional regression, little attention has been paid to the understanding of their associated feature spaces. In this paper, we explore the potential of adopting an operator-valued kernel feature space perspective for the analysis of functional data. We then extend the Regularized Least Squares Classification (RLSC) algorithm to cover situations where there are multiple functions per observation. Experiments on a sound recognition problem show that the proposed method outperforms the classical RLSC algorithm.

Domaines: Apprentissage [cs.LG]; Machine Learning [stat.ML]

Hachem Kadri, Asma Rabaoui, Philippe Preux, Emmanuel Duflos, Alain Rakotomamonjy. Functional Regularized Least Squares Classi cation with Operator-valued Kernels. 28th International Conference on Machine Learning (ICML), Jun 2011, Seattle, United States. pp.993--1000. ⟨hal-00772406⟩